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SCATLAVA: Software for Computer-Assisted Transcription Learning through Algorithmic Variation and AnalysisTranscribing music is an essential part of studying jazz. This paper introduces SCATLAVA, a software framework that analyzes a transcription for difficulty and algorithmically generates variations, in an adaptive learning manner, in order to aid students in their assimilation and understanding of the musical material and vocabulary, with an emphasis on rhythmic properties to assist jazz drummers and percussionists. The key characteristics examined by the software are onset density, syncopation measure, and limb interdependence (also known as coordination), the last of which introduces the concept of and presents an equation for calculating contextual note interdependence difficulty (CNID). Algorithmic methods for analyzing and modifying each of those properties are described in detail; adjustments are made in accordance with user input at each time step in order to adapt to students' learning needs. Finally, a demonstration of the SCATLAVA software is provided, using Elvin Jones’ drum solo from “Black Nile” as the input transcription.